Colon cancer is the second leading cause of cancer deaths in the United States. American adults have 1/20 chance of developing and 1/40 chance of dying from this disease. There are approximately 150,000 new cases diagnosed each year resulting in 56,000 deaths (See e.g. P J Wingo (1995) in a paper entitled “Cancer Statistics” and published in Ca Cancer Journal Clin. 45:8-30). Previous research has shown that adenomatous polyps, particularly those larger than 1 cm in diameter, are the most likely precursor to subsequent colorectal carcinoma (See e.g. a paper by R F Thoeni et al. (1994) entitled “Polyps and cancer” and published in “Textbook of Gastrointestinal Radiology, Philadelphia. W. B. Saunders, p1160). The National Polyp Study clearly illustrated that colonoscopic removal of all identifiable polyps resulted in a decline in mortality rate between 76% and 90% compared to historical controls (See e.g. a paper by S J Winawer et. al. (1993) entitled “Prevention of colorectal cancer by colonoscopic polypectomy” and published in N. Engl. J. Med. 329:1977-1981). Unfortunately, colon cancer is most often discovered after the patient develops symptoms, and by then, the likelihood of a cure has diminished substantially.
Fiberoptic colonoscopy (FOC) is considered the definitive diagnostic test (See e.g. a paper by D M Eddy (1990) entitled “Screening for colorectal cancer” and published in Ann. Intern. Med., 113 373-384) for the presence of colonic polyps as it affords direct visualization and the opportunity for biopsy or removal of suspicious lesions (See e.g. the referenced paper supra by Winawer et. al.). However, FOC is not feasible as a population screening test due to cost, the small but real risk of complications such as perforation, and due to the fact that there are not sufficient endoscopists in the country to accommodate all patients. Moreover, the majority of colonoscopic examinations performed in the United States are negative for polyps or masses, therefore, a less invasive, more widely available procedure that is also acceptable to patients is attractive.
Computed tomography colonography (CTC) (also referred to as virtual colonoscopy) is a recently proposed non-invasive technique that combines spiral CT data acquisition of the air-filled and cleansed colon with 3D imaging software to create virtual endoscopic images of the colonic surface. The initial clinical results are quite promising, yet the technique is still impractical due, in part, to the time required to review hundreds of images per patient study. This limitation begs for a computer-aided detection (CAD) method to help the radiologist detect polyps efficiently from the acquired CTC data.
Identifying colonic polyps using CAD is challenging because they come in various sizes and shapes, and because thickened folds and retained stool may mimic their shape and density. FIG. 1 demonstrates the appearance of polyps and healthy tissue as they appear in a virtual colonoscopy study.
Initial studies describing CAD for polyp detection have focused on shape analysis and started from the intuitive observation on the similarity of the polyp shape to hemispheres. Summers et al. characterized the colon wall by computing its minimum, maximum, mean and Gaussian curvatures (See a paper by R M Summers et al. (2000) entitled “Polypoid lesions of airways: early experience with computer-assisted detection by using virtual bronchoscopy and surface curvature” and published in Radiology 208(2):331-337). Following discrimination of polypoid shapes by their principal minimum and maximum curvatures, more restrictive criteria such as sphericity measures are applied in order to eliminate non-spherical shapes. In Yoshida et al. (H. Yoshida et al. (2000) in a paper entitled “Detection of colonic polyps in CT colonography based on geometric features” and published in Radiology 217(SS):582-582) use topological shape of vicinity of each voxel, in addition with a measure for the shape curvedness to distinguish polyps from healthy tissue. Gokturk and Tomasi designed a method based on the observation that the bounding surfaces of polyps are usually not exact spheres, but are often complex surfaces composed of small, approximately spherical patches (See S B Gokturk & C Tomasi (2000) in a paper entitled “A graph method for the conservative detection of polyps in the colon” and published at the 2nd International Symposium on Virtual Colonoscopy November 2000 Boston, USA). In this method, a sphere is fit locally to the isodensity surface passing through every CT voxel in the wall region. Groups of voxels that originate densely populated nearby sphere centers are considered as polyp candidates. Obviously, the clusters of the sphere centers are denser when the underlying shape is a sphere or a hemisphere.
Paik et al. introduced a method based on the concept that normals to the colon surface will intersect with neighboring normals depending on the local curvature features of the colon (D S Paik et al. (2000) in a paper entitled “Computer aided detection of polyps in CT colonography: Free response roc evaluation of performance” and published in Radiology 217(SS)370 and D S Paik et al. (1999) in a paper entitled “Detection of polyps in CT colonography. A comparison of a computer aided detection algorithm to 3-D visualization methods” and published in Radiological Society of North America 85th Scientific Sessions. Chicago, Ill.: Radiological Soc. N. Amer. p. 428). This method is based the observation that polyps have 3-D shape features that change rapidly in many directions, so that normals to the surface tend to intersect in a concentrated area. By contrast, haustral folds change their shape rapidly when sampled transversely, resulting in convergence of normals, but change shape very slowly when sampled longitudinally. As a result, the method detects the polyps by giving the shapes a score based on the number of intersecting normal vectors. This score is higher in hemispherical polyps compared with folds.
While most of the current methods have demonstrated promising sensitivity (i.e. ability to detect positives), they can be considered more as polyp-candidate detectors than polyp detectors because of their large number of false positive detections. Some of the methods incorporate simple intuitions about the shapes of polyps and non-polyps, which leads to false positive detections. For instance it has been observed that polyps have spherical shapes and provided different measures of sphericity. However, polyps span a large variety of shapes, and fitting spheres alone is not an accurate measure. As mentioned supra, polyp recognition is a difficult problem, but so is the manual identification of discriminating criteria between polyps and healthy tissue. Therefore, if an automatic method merely results in identifying polyp candidates, then manual examination of a (potentially large) number of images corresponding to the CAD outputs is required to ensure proper polyp detection. Such an examination is costly, time consuming and inefficient. Accordingly, there is need for new detection methods to recognize polyps in medical images and differentiate these polyps from healthy tissue.